Method of identifying parameter of characteristic of muscle, and walking assistance apparatuses and method based on the method

ABSTRACT

Disclosed is a method of identifying a parameter of a characteristic of a muscle, and walking assistance apparatuses and method based on the method. The method of identifying a parameter of a characteristic of a muscle includes estimating a first torque of a joint based on an electromyogram (EMG) signal, estimating a second torque of the joint based on motion data, and identifying a parameter of a characteristic of a muscle associated with the joint based on the first torque and the second torque.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a divisional application of U.S. application Ser.No. 15/468,444, filed on Mar. 24, 2017, which claims under 35 U.S.C. §119 to Korean Patent Application No. 10-2016-0119200, filed on Sep. 19,2016, in the Korean Intellectual Property Office, the entire contents ofeach of which are incorporated herein by reference in their entirety.

BACKGROUND 1. Field

At least one example embodiment relates to a method of identifying aparameter of a characteristic of a muscle, walking assistanceapparatuses and/or a walking assistance method based on theidentification.

2. Description of the Related Art

With the onset of rapidly aging societies, many people may experienceinconvenience and pain from joint problems. Development in a medicalfield has brought about a life extension and thus, a high quality oflife based on a healthy life has been regarded as important. In thiscontext, there has been provided services for assisting the elderly orpatients with normal activities. For example, interest in motionassistance apparatuses enabling the elderly or patients with jointproblems to walk with less effort, may increase.

In general, motion assistance apparatuses for assisting motion of lowerparts of bodies may include body frames disposed on trunks of users,pelvic frames coupled to lower sides of the body frames to coverpelvises of the users, femoral frames disposed on thighs of the users,sural frames disposed on calves of the users, and pedial frames disposedon feet of the users. The pelvic frames and femoral frames may beconnected rotatably by hip joint portions, the femoral frames and suralframes may be connected rotatably by knee joint portions, and the suralframes and pedial frames may be connected rotatably by ankle jointportions.

However, due to various musculoskeletal and nerve conditions of humanbody, technology for cooperating with the human body without a damage ofthe human body is still insufficient and thus, continuous research onmotion assistance apparatuses is required.

There has been a lot of effort for providing a personalized algorithmmatching various body structures and musculoskeletal and nervedeteriorating conditions of individuals. To this end, a customizingalgorithm of adjusting details may be applied for each person ingeneral. However, personnel expenses and service costs may increase insuch case. Thus, it is difficult to realize a product in a massproduction type and also difficult to reduce a price of the product.

SUMMARY

Some example embodiments relate to a method of identifying a parameterassociated with a characteristic of a muscle.

In some example embodiment, the method may include estimating a firsttorque of a joint of a user based on an electromyogram (EMG) signal of amuscle associated with the joint; estimating a second torque of thejoint based on motion data; and identifying a parameter of acharacteristic of the muscle associated with the joint based on thefirst torque and the second torque.

In some example embodiments, the muscle includes at least one of asoleus muscle, a tibialis anterio muscle, a gastrocnemius muscle, avastus lateralis muscle, a hamstring muscle, a gluteus maximus muscle,and a hip flexor muscle.

In some example embodiments, the parameter includes at least one of amuscular strength, an optimal length, a slack length, and a movingvelocity of the muscle.

In some example embodiments, the identifying includes: updating aninitial version of the parameter of the muscle based on a differencebetween the first torque and the second torque to generate an updatedparameter; and repetitively estimating the first torque using theupdated parameter.

In some example embodiments, the estimating of the first torque includesestimating the first torque by applying the EMG signal to muscledynamics, and the estimating of the second torque includes estimatingthe second torque by applying the motion data to body dynamics.

Some example embodiments relate to a walking assistance method.

In some example embodiments, the walking assistance method includesidentifying a parameter of a characteristic of a muscle associated witha joint based on a first torque of the joint and a second torque of thejoint, the first torque being estimated based on an electromyogram (EMG)signal and the second torque being estimated based on motion data; andcontrolling a walking assistance apparatus based on a gait typedetermined using the parameter.

In some example embodiments, the controlling includes: diagnosing agait-related disease corresponding to the characteristic of the musclebased on the identified parameter; and controlling the walkingassistance apparatus based on the gait type corresponding to thegait-related disease.

In some example embodiments, the controlling includes: controlling thewalking assistance apparatus based on the gait type selected from amonga plurality of abnormal gait types based on the parameter.

In some example embodiments, the muscle includes at least one of asoleus muscle, a tibialis anterio muscle, a gastrocnemius muscle, avastus lateralis muscle, a hamstring muscle, a gluteus maximus muscle,and a hip flexor muscle.

In some example embodiments, the parameter includes at least one of amuscular strength, an optimal length, a slack length, and a movingvelocity of the muscle.

In some example embodiments, the identifying includes: updating aninitial parameter of the muscle based on a difference between the firsttorque and the second torque to generate an updated parameter; andextracting the parameter of the muscle by repetitively estimating thefirst torque using the updated parameter.

In some example embodiments, the identifying includes: estimating thefirst torque by applying the EMG signal to muscle dynamics; estimatingthe second torque by applying the motion data to body dynamics; andidentifying the parameter of the characteristic of the muscle associatedwith the joint using the first torque and the second torque.

Some example embodiments relate to an apparatus configured to identify aparameter.

In some example embodiments, the apparatus includes an interfaceconfigured to acquire an electromyogram (EMG) signal and motion data;and a controller configured to, estimate a first torque of a joint basedon the EMG signal, estimate a second torque of the joint based on themotion data, and identify the parameter of a characteristic of a muscleassociated with the joint based on the first torque and the secondtorque.

In some example embodiments, the muscle includes at least one of asoleus muscle, a tibialis anterio muscle, a gastrocnemius muscle, avastus lateralis muscle, a hamstring muscle, a gluteus maximus muscle,and a hip flexor muscle.

In some example embodiments, the parameter includes at least one of amuscular strength, an optimal length, a slack length, and a movingvelocity of the muscle.

In some example embodiments, the controller is configured to, update aninitial parameter of the muscle based on a difference between the firsttorque and the second torque to generate an updated parameter, andextract the parameter of the muscle by repetitively estimating the firsttorque using the updated parameter.

In some example embodiments, the controller is configured to, estimatethe first torque by applying the EMG signal to muscle dynamics, andestimate the second torque by applying the motion data to body dynamics.

Additional aspects of example embodiments will be set forth in part inthe description which follows and, in part, will be apparent from thedescription, or may be learned by practice of the disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

These and/or other aspects will become apparent and more readilyappreciated from the following description of example embodiments, takenin conjunction with the accompanying drawings of which:

FIG. 1 illustrates an example of a musculoskeletal model for identifyinga parameter of a characteristic of a muscle according to at least oneexample embodiment;

FIG. 2A illustrates an example of a heel-type muscle model according toat least one example embodiment;

FIG. 2B illustrates an example of characteristics associated with alength and a speed of the heel-type muscle model of FIG. 2A;

FIG. 3 is block diagram illustrating an example of a system including aparameter identification apparatus for identifying a parameter of acharacteristic of a muscle according to at least one example embodiment;

FIG. 4 is a block diagram illustrating the parameter identificationapparatus of FIG. 3;

FIG. 5 is a flowchart illustrating an operation of the parameteridentification apparatus of FIG. 3;

FIG. 6 is block diagram illustrating another example of a systemincluding the parameter identification apparatus of FIG. 3;

FIG. 7 is a block diagram illustrating a walking assistance apparatus ofFIG. 6;

FIG. 8 is a front view of a target body wearing the walking assistanceapparatus of FIG. 6; and

FIG. 9 is a side view of a target body wearing the walking assistanceapparatus of FIG. 6.

DETAILED DESCRIPTION

Hereinafter, some example embodiments will be described in detail withreference to the accompanying drawings. Regarding the reference numeralsassigned to the elements in the drawings, it should be noted that thesame elements will be designated by the same reference numerals,wherever possible, even though they are shown in different drawings.Also, in the description of embodiments, detailed description ofwell-known related structures or functions will be omitted when it isdeemed that such description will cause ambiguous interpretation of thepresent disclosure.

It should be understood, however, that there is no intent to limit thisdisclosure to the particular example embodiments disclosed. On thecontrary, example embodiments are to cover all modifications,equivalents, and alternatives falling within the scope of the exampleembodiments. Like numbers refer to like elements throughout thedescription of the figures.

In addition, terms such as first, second, A, B, (a), (b), and the likemay be used herein to describe components. Each of these terminologiesis not used to define an essence, order or sequence of a correspondingcomponent but used merely to distinguish the corresponding componentfrom other component(s). It should be noted that if it is described inthe specification that one component is “connected”, “coupled”, or“joined” to another component, a third component may be “connected”,“coupled”, and “joined” between the first and second components,although the first component may be directly connected, coupled orjoined to the second component.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting. As used herein, thesingular forms “a,” “an,” and “the,” are intended to include the pluralforms as well, unless the context clearly indicates otherwise. It willbe further understood that the terms “comprises,” “comprising,”“includes,” and/or “including,” when used herein, specify the presenceof stated features, integers, steps, operations, elements, and/orcomponents, but do not preclude the presence or addition of one or moreother features, integers, steps, operations, elements, components,and/or groups thereof.

It should also be noted that in some alternative implementations, thefunctions/acts noted may occur out of the order noted in the figures.For example, two figures shown in succession may in fact be executedsubstantially concurrently or may sometimes be executed in the reverseorder, depending upon the functionality/acts involved.

Various example embodiments will now be described more fully withreference to the accompanying drawings in which some example embodimentsare shown. In the drawings, the thicknesses of layers and regions areexaggerated for clarity.

FIG. 1 illustrates an example of a musculoskeletal model for identifyinga parameter of a characteristic of a muscle according to at least oneexample embodiment, FIG. 2A illustrates an example of a heel-type musclemodel according to at least one example embodiment, and FIG. 2Billustrates an example of characteristics associated with a length and aspeed of the heel-type muscle model of FIG. 2A.

Referring to FIGS. 1 through 2B, for each and brevity of description, amusculoskeletal model including seven muscles may be used in an exampleof FIG. 1, and a heel-type muscle model may be used in an example ofFIGS. 2A and 2B. However, the present disclosure is not limited thereto.A musculoskeletal model including at least N muscles, N being a naturalnumber greater than or equal to 1, and various types of muscle modelsare also applicable based on an example.

A human gait may be structuralized to be a gait of a musculoskeletalmodel corresponding to a human body. The musculoskeletal model may beconfigured in a human body model including frames having desired (or,alternatively, predetermined) mass and inertia moment. The frames mayconfigure parts of a head, a chest, a pelvis, a thigh, a calf, and afoot. Further, a joint may be formed in each portion connecting theframes. A muscle corresponding to a driving force may be formed betweenjoints. The muscle may include at least one of a soleus muscle SOL, atibialis anterio muscle TA, a gastrocnemius muscle GAS, a vastuslateralis muscle VAS, a hamstring muscle HAM, a gluteus maximus muscleGLU, or a hip flexor muscle HFL.

A muscle model corresponding to the muscle may be a heel-type model ofFIG. 2A. The heel-type muscle model may include a serial element SE, aparallel element PE, and a contractile element CE corresponding to amuscle tendon unit.

A force generated in the contractile element CE may be expressed byEquation 1.

F _(ce) =AF _(max) F _(l)(I _(ce))F _(v)(V _(ce))  [Equation 1]

In Equation 1, A denotes an excitation signal, and F_(max) denotes amaximal muscular strength. As illustrated in FIG. 2B, F_(l) and F_(v)respectively denote muscular strengths relative to a length and avelocity of a muscle of the contractile element CE, for example, a CEmuscle. For example, F_(l) corresponding to a value of the muscularstrength relative to the length of the CE muscle is represented by amuscle F-L graph in a left portion of FIG. 2B, and F_(v) correspondingto a value of the muscular strength relative to the velocity of the CEmuscle is represented by a muscle F-V graph in a right portion of FIG.2B. I_(ce) and V_(ce) denote the length and the velocity of the CEmuscle, respectively.

As shown by Equation 1, a micro-excitation signal A occurring in anervous system may be required to generate a force in the muscle. Theexcitation signal A may be, for example, an electromyogram (EMG) signal.The excitation signal A may trigger a positive feedback to be applied tothe muscle. Through this, the force may be generated in the muscle.

A gait may be performed based on a general muscular strength of a user.Also, the gait may be performed to secure a stability with minimalenergy consumption. A characteristic of a muscle may be used to predicta motion and/or a movement of the muscle, and thus, gait information maybe acquired by identifying the characteristic of the muscle.

A parameter of the characteristic of the muscle may be extracted frompower generated in the contractile element CE and used as a factor ofdetermining the characteristic of the muscle. The parameter may includeat least one of a muscular strength, an optimal length or an optimalmuscular fiber length, a slack length, or a moving speed of the muscle.The motion and/or the movement of the muscle may be predicted through anidentification of the parameter of the characteristic of the muscle.

For example, the parameter of the muscle may represent a human gait. Byidentifying the parameter of the muscle, information associated with asource of generating the human gait may be acquired in lieu of acquiringa gait as coordinates of a trajectory and a time at a domaincorresponding to a superficial location. As such, by identifying a valueof the parameter, a user or a gait of the user may be identified withincreased accuracy.

As the foregoing, the human gait may be structured to be a gait of themusculoskeletal model, and a type of the gait may be represented througha parameter conversion of the characteristic of the muscle. For example,the gait may also be represented as a set of parameters associated withthe characteristic of the muscle.

FIG. 3 is block diagram illustrating an example of a system including aparameter identification apparatus for identifying a parameter of acharacteristic of a muscle according to at least one example embodiment,and FIG. 4 is a block diagram illustrating the parameter identificationapparatus of FIG. 3.

Referring to FIGS. 3 and 4, a system 10 may be a system for identifyinga parameter of a characteristic of a muscle of human. The system 10 mayinclude a first sensor 100, a second sensor 200, and a parameteridentification apparatus 300.

The first sensor 100 may sense an EMG signal of a muscle of human. Thefirst sensor 100 may be implemented as at least one EMG sensor. Forexample, an EMG sensor may be provided for each muscle of which an EMGsignal is to be sensed. The first sensor 100 may transmit the EMG signalto the parameter identification apparatus 300.

The second sensor 200 may generate motion data of human. For example,the second sensor 200 may generate or measure motion data includinginformation on a length and a position of a joint of human andinformation on a joint movement based on a gait. The second sensor 200may be, for example, a motion capturing device and/or a force platedevice. The second sensor 200 may transmit the motion data to theparameter identification apparatus 300.

Although FIG. 3 illustrates the first sensor 100 and the second sensor200 disposed externally to the parameter identification apparatus 300 asan example, the disclosure is not limited thereto. Depending on anexample, the first sensor 100 and the second sensor 200 may also beincluded in the parameter identification apparatus 300.

The parameter identification apparatus 300 may be an apparatus foridentifying a parameter of a characteristic of a muscle.

For example, the muscle may include at least one of a soleus muscle, atibialis anterio muscle, a gastrocnemius muscle, a vastus lateralismuscle, a hamstring muscle, a gluteus maximus muscle, or a hip flexormuscle. The parameter may include at least one of a muscular strength,an optimal length or an optimal muscular fiber length, a slack length,or a moving speed of the muscle.

Referring to FIG. 4, the parameter identification apparatus 300 mayinclude an interface 310 and a controller 330.

The interface 310 may include a transmitting device having hardware andany necessary software for transmitting signals including, for example,data signals and control signals, and a receiving device having hardwareand any necessary software for receiving signals including, for example,data signals and control signals.

The interface 310 may acquire or receive an EMG signal and motion data.For example, the interface 310 may acquire the EMG signal from the firstsensor 100. Also, the interface 310 may acquire the motion data from thesecond sensor 200. The interface 310 may transmit the acquired EMGsignal and motion data to the controller 330.

The controller 330 may include any device capable of processing dataincluding, for example, an application application-specific integratedcircuit (ASIC) configured to carry out specific operations based oninput data, or a microprocessor configured as a special purposeprocessor by executing instructions included in computer readable code.The computer readable code may be stored on, for example, a memory (notshown). As discussed in more detail below with reference to FIG. 5, thecomputer readable code may configure the controller 330 to identify aparameter of a characteristic of the muscle associated with a jointbased on a first torque and a second torque.

The controller 330 may estimate a torque of a joint associated orconnected with a muscle based on the EMG signal and the motion data.

The controller 330 may estimate or calculate the first torque of a jointbased on the EMG signal. The joint may indicate a joint associated orconnected with the muscle. For example, the controller 330 may estimatethe first torque by applying the EMG signal to muscle dynamics.

In one example, the controller 330 may estimate the first torque bysubstituting the EMG signal in Equation 1. In another example, thecontroller 330 may estimate the first torque by performing dynamicsimulation using the musculoskeletal model of FIG. 1 in response to theEMG signal. In the dynamic simulation, the EMG signal may be anactivation signal or an excitation signal activating a muscle of themusculoskeletal model or an activation signal.

The controller 330 may estimate the second torque of the joint based onthe motion data. For example, the controller 330 may estimate the secondtorque by applying the motion data to body dynamics.

The controller 330 may identify the parameter of the characteristic ofthe muscle associated or connected with the joint based on the firsttorque and the second torque. For example, the controller 330 mayrepetitively estimate the first torque by adjusting the parameterthrough an optimizing process based on a difference between the firsttorque and the second torque. In this example, the controller 330 mayextract an optimal parameter by performing the optimizing process toreduce (or, alternatively, minimize) the difference based on a variationin difference relative to a variation in parameter.

FIG. 5 is a flowchart illustrating an operation of the parameteridentification apparatus of FIG. 3.

Referring to FIG. 5, in operation 510, the controller 330 may estimate afirst torque by applying, to muscle dynamics, an EMG signal and aparameter of a characteristic of a muscle which is set initially.

In operation 520, the controller 330 may estimate a second torque byapplying motion data to body dynamics.

In operation 530, the controller 330 may extract or calculate adifference between the first torque and the second torque. In operation540, the controller 330 may verify whether the difference is a reduced(or, alternatively, a minimal) value.

In operation 550, when the difference is not the reduced (or,alternatively, the minimal) value, the controller 330 may update theparameter. The controller 330 may perform operations 510 through 540using the updated parameter. The controller 330 may repetitively performoperations 510 through 550 until the difference is minimized.

The controller 330 may extract the parameter used when the difference isminimized, to be the optimal parameter of the characteristic of themuscle. The parameter may include at least one of a muscular strength,an optimal length or an optimal muscular fiber length, a slack length,or a moving speed of the muscle.

In this example, a time when the difference is reduced (or,alternatively, minimized) may indicate a state in which a change indifference is absent in a process of repetitively performing operations510 through 550, that is, a state in which a variation in difference isreduced (or, alternatively, minimized).

As described with reference to FIGS. 1 through 5, the parameteridentification apparatus 300 may parameterize situations associated witha muscle and a gait nerve of a user based on an EMG signal and gaitdata, for example, motion data, and acquire a condition and a gait stateof the user. Through this, a walking assistance apparatus may perform acustomization with a minimal effort.

The foregoing example may be applicable to a method of diagnosing ahuman musculoskeletal disease, and applicable to a security and a methodto provide a personalized Internet of things (IoT) service and perform aremote user identification as technology for identifying a user byacquiring gait feature of a user based on human gait data. Also, with anavailability of remote recognition, the foregoing example may beapplicable to a medical field to identify users suffering from a diseasethrough a gait identification for each user, track a gait using agesture recognition-based video device without need to perform aninvasive inspection, and identify a patient group to which a gait of auser belongs.

Hereinafter, a walking assistance apparatus using a parameter identifiedby the parameter identification apparatus 300 will be described.

FIG. 6 is block diagram illustrating another example of a systemincluding the parameter identification apparatus of FIG. 3.

Referring to FIG. 6, a walking assistance system 600 may include awalking assistance apparatus 700 and a parameter identificationapparatus 300. In this disclosure, the term “walking” may beinterchangeably used as a term “gait”.

The parameter identification apparatus 300 may identify a parameter of acharacteristic of a muscle associated with a joint using a first torqueof the joint estimated based on an EMG signal and a second torque of thejoint estimated based on motion data. A configuration and an operationof the parameter identification apparatus 300 of FIG. 6 may besubstantially the same as the configuration and operation of theparameter identification apparatus 300 described with reference to FIGS.1 through 5 and thus, repeated descriptions will be omitted.

The parameter identification apparatus 300 may transmit the identifiedparameter to the walking assistance apparatus 700.

The walking assistance apparatus 700 may be worn by a target body, forexample, a user, to assist the user during an exercise and/or walking.The target body may be, for example, a person, an animal, and a robot,however, an example of the target body is not limited thereto.

The walking assistance apparatus 700 may assist a gait and/or a motionof, for example, a hand, an upper arm, a lower arm, and the other partof an upper body of the user. Alternatively, the walking assistanceapparatus 700 may assist a gait and/or a motion of, for example, a foot,a calf, a thigh, and the other part of a lower body of the user. Thus,the walking assistance apparatus 700 may assist a gait and/or a motionof a part of the user.

The walking assistance apparatus 700 may diagnose a gait-related diseasecorresponding to the characteristic of the muscle based on theidentified parameter. Also, the walking assistance apparatus 700 may becontrolled based on a gait type corresponding to the diagnosedgait-related disease. The gait-related disease may include, for example,a joint inflammation and a cerebral infarction.

Also, the walking assistance apparatus 700 may be controlled based on agait type corresponding to the characteristic of the muscle among aplurality of abnormal gait types based on the identified parameter.

An abnormal gait may indicate a gait evolved to continue an abnormal orpathological gait pattern when a normal gait pattern collapses as aresult of a functional disorder due to, for example, a partial damage,weakness, a loss of flexibility, a pain, a bad habit, and a neural ormuscular injury. The abnormal gait may indicate, for example, apathological gait pattern. In this disclosure, the term “abnormal” maybe interchangeably used with the term “pathological”.

In an example, the at least one abnormal gait type may include at leastone of a crouch gait or genu recurvatum gait, a steppage gait orfootdrop gait, an antalgic gait, an ataxic gait, a festinating gait, avaulting gait, a lurching gait, an equinus gait, a short leg gait, ahemiplegic gait, a circumduction gait, a tabetic gait, a neurogenicgait, a scissoring gait, and a Parkinsonian gait. The lurching gait mayindicate any form of staggering gait and include, for example, awaddling gait, a gluteus maximus gait, and a trendelenburg gait. Thewaddling gait may indicate a gait characterized in swaying from side toside. The gluteus maximus gait may indicate a gait in which a chest isbent backward to maintain a hip extension and a trunk movement issuddenly exaggerated to walk from time to time. The trendelenburg gaitmay indicate a gait performed by tilting a chest toward a weakened legto maintain a center of gravity and prevent a pelvis of a weakened sidefrom drooping when standing on the ground with a weakened lower limb.

The crouch gait may indicate a gait performed with a posture of hunchingall joints of a hip, a knee, and an ankle to overcome a gaitinstability. The steppage gait may indicate a gait in which toes arebent downward to the ground and a top of a foot is dropped to theground. The antalgic gait may indicate a gait for avoiding a pain on apainful portion. The ataxic gait may indicate a gait characterized by anunsteady stride, a wide space between feet, a shaken body, and anunstable step appearing intoxicated. The festinating gait may indicate agait performed with stiff arms, a trunk flexed forward, a short stance,and accelerating steps as if unbreakable. The vaulting gait may indicatea gait using a leg of a non-affected side, for example, a non-paralyzedside, in lieu of a leg of an affected side, for example, a paralyzedside when a knee joint is not extendable. The equines gait may indicatea gait performed using tiptoes while heels are not in contact with theground. The hemiplegic gait may indicate a gait in which, due to astiffness, an entire body is slightly tilted to the affected side, aswing of an upper arm in the affected side is lost, a shoulder of theaffected side is in a descending state, and the lower limb appears in aprimitively curved form. The circumduction gait may indicate a gait inwhich an entire leg swings due to a difficulty in bending the knee. Thescissoring gait may indicate a gait performed by crossing or grazinglegs or knees against to one another with a squatting posture in a statein which the legs are slightly bent inward. The Parkinsonian gait mayindicate a gait performed as if shuffling a sole on the ground with ananterior flexion posture.

Although FIG. 6 illustrates the parameter identification apparatus 300disposed externally to the walking assistance apparatus 700 as anexample, the disclosure is not limited thereto. Depending on an example,the parameter identification apparatus 300 may also be included in thewalking assistance apparatus 700.

FIG. 7 is a block diagram illustrating a walking assistance apparatus ofFIG. 6, FIG. 8 is a front view of a target body wearing the walkingassistance apparatus of FIG. 6, and FIG. 9 is a side view of a targetbody wearing the walking assistance apparatus of FIG. 6.

Referring to FIGS. 6 through 9, the walking assistance apparatus 700 mayinclude a controller 710 and a driver 730. The walking assistanceapparatus 700 may also include a fixing member 740, a force transmittingmember 750, and a supporting member 760.

Although FIGS. 8 and 9 illustrate the walking assistance apparatus 700,for example, a hip-type walking assistance apparatus, operating on athigh of a user 800, the type of the walking assistance apparatus 700 isnot limited thereto. The walking assistance apparatus 700 may beapplicable to, for example, a walking assistance apparatus that supportsan entire pelvic limb, a walking assistance apparatus that supports aportion of a pelvic limb, and the like. The walking assistance apparatusthat supports a portion of a pelvic limb may be applicable to, forexample, a walking assistance apparatus that supports up to a knee, anda walking assistance apparatus that supports up to an ankle.

The controller 710 may include any device capable of processing dataincluding, for example, an application application-specific integratedcircuit (ASIC) configured to carry out specific operations based oninput data, or a microprocessor configured as a special purposeprocessor by executing instructions included in computer readable code.The computer readable code may be stored on, for example, a memory (notshown). For example, the computer readable code may configure thecontroller 710 as a special purpose processor to receive anidentification parameter from the parameter identification apparatus300, where the identification parameter indicates at least one of amuscular strength, an optimal length, a slack length, and a movingvelocity of a muscle, and to generate an assist torque profile based ona gait type corresponding to a characteristic of the muscle among aplurality of abnormal gait types based on the identified parameter.

The controller 710 may control an overall operation of the walkingassistance apparatus 700. For example, the controller 710 may controlthe driver 730 to output a driving force to assist a gait of the user800. The driving force may be, for example, an assistance torque.

The controller 710 may diagnose a gait-related disease corresponding toa characteristic of a muscle based on an identified parameter, andgenerate an assist torque profile based on a gait type corresponding tothe diagnosed gait-related disease.

Also, the controller 710 may generate an assist torque profile based ona gait type corresponding to a characteristic of a muscle among aplurality of abnormal gait types based on an identified parameter.

The driver 730 may be disposed on one or more of a left hip portion anda right hip portion of the user 800 to drive one or more of the hipjoints of the user 800. The driver 730 may generate a force to assist agait of the user 800 based on the assist torque profile generated in thecontroller 710.

The fixing member 740 may be attached to a part, for example, a waist ofthe user 800. The fixing member 740 may be in contact with at least aportion of an external surface of the user 800. The fixing member 740may cover along the external surface of the user 800.

The force transmitting member 750 may connect the driver 730 and thesupporting member 760. The force transmitting member 750 may transmitthe driving force received from the driver 730 to the supporting member760. As an example, the force transmitting member 750 may be alongitudinal member such as, for example, a wire, a cable, a string, arubber band, a spring, a belt, and a chain.

The supporting member 760 may support a target part, for example, athigh of the user 800. The supporting member 760 may be disposed tocover at least a portion of the user 800. The supporting member 760 mayexert a force on the target part of the user 800 using the driving forcereceived from the force transmitting member 750.

The units and/or modules described herein may be implemented usinghardware components and software components. For example, the hardwarecomponents may include microphones, amplifiers, band-pass filters, audioto digital convertors, and processing devices. A processing device maybe implemented using one or more hardware device configured to carry outand/or execute program code by performing arithmetical, logical, andinput/output operations. The processing device(s) may include aprocessor, a controller and an arithmetic logic unit, a digital signalprocessor, a microcomputer, a field programmable array, a programmablelogic unit, a microprocessor or any other device capable of respondingto and executing instructions in a defined manner. The processing devicemay run an operating system (OS) and one or more software applicationsthat run on the OS. The processing device also may access, store,manipulate, process, and create data in response to execution of thesoftware. For purpose of simplicity, the description of a processingdevice is used as singular; however, one skilled in the art willappreciated that a processing device may include multiple processingelements and multiple types of processing elements. For example, aprocessing device may include multiple processors or a processor and acontroller. In addition, different processing configurations arepossible, such a parallel processors.

The software may include a computer program, a piece of code, aninstruction, or some combination thereof, to independently orcollectively instruct and/or configure the processing device to operateas desired, thereby transforming the processing device into a specialpurpose processor. Software and data may be embodied permanently ortemporarily in any type of machine, component, physical or virtualequipment, computer storage medium or device, or in a propagated signalwave capable of providing instructions or data to or being interpretedby the processing device. The software also may be distributed overnetwork coupled computer systems so that the software is stored andexecuted in a distributed fashion. The software and data may be storedby one or more non-transitory computer readable recording mediums.

The methods according to the above-described example embodiments may berecorded in non-transitory computer-readable media including programinstructions to implement various operations of the above-describedexample embodiments. The media may also include, alone or in combinationwith the program instructions, data files, data structures, and thelike. The program instructions recorded on the media may be thosespecially designed and constructed for the purposes of exampleembodiments, or they may be of the kind well-known and available tothose having skill in the computer software arts. Examples ofnon-transitory computer-readable media include magnetic media such ashard disks, floppy disks, and magnetic tape; optical media such asCD-ROM discs, DVDs, and/or Blue-ray discs; magneto-optical media such asoptical discs; and hardware devices that are specially configured tostore and perform program instructions, such as read-only memory (ROM),random access memory (RAM), flash memory (e.g., USB flash drives, memorycards, memory sticks, etc.), and the like. Examples of programinstructions include both machine code, such as produced by a compiler,and files containing higher level code that may be executed by thecomputer using an interpreter. The above-described devices may beconfigured to act as one or more software modules in order to performthe operations of the above-described example embodiments, or viceversa.

A number of example embodiments have been described above. Nevertheless,it should be understood that various modifications may be made to theseexample embodiments. For example, suitable results may be achieved ifthe described techniques are performed in a different order and/or ifcomponents in a described system, architecture, device, or circuit arecombined in a different manner and/or replaced or supplemented by othercomponents or their equivalents. Accordingly, other implementations arewithin the scope of the following claims.

What is claimed is:
 1. A walking assistance method comprising:identifying a parameter of a characteristic of a muscle associated witha joint based on a first torque of the joint and a second torque of thejoint, the first torque being estimated based on an electromyogram (EMG)signal and the second torque being estimated based on motion data; andcontrolling a walking assistance apparatus based on a gait typedetermined using the parameter.
 2. The walking assistance method ofclaim 1, wherein the controlling comprises: diagnosing a gait-relateddisease corresponding to the characteristic of the muscle based on theidentified parameter; and controlling the walking assistance apparatusbased on the gait type corresponding to the gait-related disease.
 3. Thewalking assistance method of claim 1, wherein the controlling comprises:controlling the walking assistance apparatus based on the gait typeselected from among a plurality of abnormal gait types based on theparameter.
 4. The walking assistance method of claim 1, wherein themuscle includes at least one of a soleus muscle, a tibialis anteriomuscle, a gastrocnemius muscle, a vastus lateralis muscle, a hamstringmuscle, a gluteus maximus muscle, and a hip flexor muscle.
 5. Thewalking assistance method of claim 1, wherein the parameter includes atleast one of a muscular strength, an optimal length, a slack length, anda moving velocity of the muscle.
 6. The walking assistance method ofclaim 1, wherein the identifying comprises: updating an initialparameter of the muscle based on a difference between the first torqueand the second torque to generate an updated parameter; and extractingthe parameter of the muscle by repetitively estimating the first torqueusing the updated parameter.
 7. The walking assistance method of claim1, wherein the identifying comprises: estimating the first torque byapplying the EMG signal to muscle dynamics; estimating the second torqueby applying the motion data to body dynamics; and identifying theparameter of the characteristic of the muscle associated with the jointusing the first torque and the second torque.
 8. A walking assistanceapparatus, the apparatus comprising: a driver configured to output adriving force; a controller configured to, identify a parameter of acharacteristic of a muscle associated with a joint based on a firsttorque of the joint and a second torque of the joint, the first torquebeing estimated based on an electromyogram (EMG) signal and the secondtorque being estimated based on motion data, and control the driverbased on a gait type determined using the parameter.
 9. The walkingassistance apparatus of claim 8, wherein the controller is configuredto, diagnose a gait-related disease corresponding to the characteristicof the muscle based on the identified parameter, and control the driverbased on the gait type corresponding to the gait-related disease. 10.The walking assistance apparatus of claim 8, wherein the controller isconfigured to control the driver based on the gait type selected fromamong a plurality of abnormal gait types based on the parameter.
 11. Thewalking assistance apparatus of claim 8, wherein the muscle includes atleast one of a soleus muscle, a tibialis anterio muscle, a gastrocnemiusmuscle, a vastus lateralis muscle, a hamstring muscle, a gluteus maximusmuscle, and a hip flexor muscle.
 12. The walking assistance apparatus ofclaim 8, wherein the parameter includes at least one of a muscularstrength, an optimal length, a slack length, and a moving velocity ofthe muscle.